EU carbon pricing and clean energy markets: evidence from cross-quantilogram and multivariate time-varying quantile regression
- 1Department of Management Information Systems, Istanbul Ticaret University, Istanbul, Turkey
- 2Nizami Gajanvi Research Centre of Sustainable Development & Green Economy, Azerbaijan State University of Economics, Baku, Azerbaijan
- 3Advanced Research Centre, European University of Lefke, Lefke/TRNC, Northern Cyprus TR-10 Turkey
Abstract
Understanding how regionally determined carbon-pricing signals are transmitted into globally traded clean energy equities is increasingly important as climate policy and financial markets become more integrated. Yet, evidence remains limited on whether these linkages are nonlinear, state-dependent, and heterogeneous across clean energy technologies. This study examines how movements in European Union Emissions Trading System (EU-ETS) futures, a regional carbon market and policy-linked price signal, are reflected in the returns of ten globally listed clean energy subsectors using daily data from 19 October 2010 to 6 July 2024. Using disaggregated NASDAQ OMX Clean Energy subsector indices, the analysis captures how carbon-pricing information originating in the EU is incorporated into globally integrated equity markets. The study combines the Cross-Quantilogram (CQ) and Multivariate Time-Varying Quantile Regression (MTVQR) frameworks to examine directional predictability and conditional impacts, while controlling for financial uncertainty (VIX) and oil-market conditions (Brent crude oil prices). The CQ results show that EU-ETS futures contain significant predictive information for clean energy returns, with nonlinear, asymmetric dependence concentrated on the tails of the distribution. The MTVQR results further show that the conditional impact of carbon-pricing signals varies across market states and technologies. More mature subsectors, such as solar, wind, and smart grid, respond more strongly in weak and normal markets, while capital-intensive and enabling technologies, including biofuels, geothermal, storage, and fuel cells, exhibit stronger upper-tail or nonlinear responses. The findings highlight EU carbon pricing as a forward-looking transition-risk signal with implications for climate policy, portfolio allocation, and transition-risk management.
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BibTeX
@article{olasehindewilliams2026carbon,
title = {{EU carbon pricing and clean energy markets: evidence from cross-quantilogram and multivariate time-varying quantile regression}},
author = {Godwin Olasehinde-Williams and Seyi Saint Akadiri},
journal = {Carbon Balance and Management},
year = {2026},
month = jul,
doi = {10.1186/s13021-026-00492-8},
url = {https://doi.org/10.1186/s13021-026-00492-8},
note = {CC BY-NC-ND 4.0},
}
RIS
TY - JOUR
TI - EU carbon pricing and clean energy markets: evidence from cross-quantilogram and multivariate time-varying quantile regression
AU - Godwin Olasehinde-Williams
AU - Seyi Saint Akadiri
PY - 2026
DA - 2026/07/26
JO - Carbon Balance and Management
DO - 10.1186/s13021-026-00492-8
UR - https://doi.org/10.1186/s13021-026-00492-8
AB - Understanding how regionally determined carbon-pricing signals are transmitted into globally traded clean energy equities is increasingly important as climate policy and financial markets become more integrated. Yet, evidence remains limited on whether these linkages are nonlinear, state-dependent, and heterogeneous across clean energy technologies. This study examines how movements in European Union Emissions Trading System (EU-ETS) futures, a regional carbon market and policy-linked price signal, are reflected in the returns of ten globally listed clean energy subsectors using daily data from 19 October 2010 to 6 July 2024. Using disaggregated NASDAQ OMX Clean Energy subsector indices, the analysis captures how carbon-pricing information originating in the EU is incorporated into globally integrated equity markets. The study combines the Cross-Quantilogram (CQ) and Multivariate Time-Varying Quantile Regression (MTVQR) frameworks to examine directional predictability and conditional impacts, while controlling for financial uncertainty (VIX) and oil-market conditions (Brent crude oil prices). The CQ results show that EU-ETS futures contain significant predictive information for clean energy returns, with nonlinear, asymmetric dependence concentrated on the tails of the distribution. The MTVQR results further show that the conditional impact of carbon-pricing signals varies across market states and technologies. More mature subsectors, such as solar, wind, and smart grid, respond more strongly in weak and normal markets, while capital-intensive and enabling technologies, including biofuels, geothermal, storage, and fuel cells, exhibit stronger upper-tail or nonlinear responses. The findings highlight EU carbon pricing as a forward-looking transition-risk signal with implications for climate policy, portfolio allocation, and transition-risk management.
SN - 1750-0680
ER -